Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction
The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue,...
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MDPI AG
2024-10-01
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| author | Batoul El-Sayegh Chantale Dumoulin François Leduc-Primeau Mohamad Sawan |
| author_facet | Batoul El-Sayegh Chantale Dumoulin François Leduc-Primeau Mohamad Sawan |
| author_sort | Batoul El-Sayegh |
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| description | The first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>1</mn></mrow></semantics></math></inline-formula> scale point with 97% accuracy. These results demonstrate the system’s potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously. |
| format | Article |
| id | doaj-art-41f4fca3efb04344808c0743ee91995b |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-41f4fca3efb04344808c0743ee91995b2024-11-08T14:41:32ZengMDPI AGSensors1424-82202024-10-012421693710.3390/s24216937Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor DysfunctionBatoul El-Sayegh0Chantale Dumoulin1François Leduc-Primeau2Mohamad Sawan3Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, CanadaResearch Center, Institut Universtaire de Gériatrie de Montréal, Montreal, QC H3W 1W5, CanadaDepartment of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, CanadaDepartment of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, CanadaThe first line of treatment for urinary incontinence is pelvic floor muscle (PFM) training, aimed at reducing leakage episodes by strengthening these muscles. However, many women struggle with performing correct PFM contractions or have misconceptions about their contractions. To address this issue, we present a novel PFM contraction quality assessment system. This system combines a PFM contraction detector with a maximal PFM contraction performance classifier. The contraction detector first identifies whether or not a PFM contraction was performed. Then, the contraction classifier autonomously quantifies the quality of maximal PFM contractions across different features, which are also combined into an overall rating. Both algorithms are based on artificial intelligence (AI) methods. The detector relies on a convolutional neural network, while the contraction classifier uses a custom feature extractor followed by a random forest classifier to predict the strength rating based on the modified Oxford scale. The AI algorithms were trained and tested using datasets measured by vaginal dynamometry, combined in some cases with digital assessment results from expert physiotherapists. The contraction detector was trained on one dataset and then tested on two datasets measured with different dynamometers, achieving 97% accuracy on the first dataset and 100% accuracy on the second. For the contraction performance classifier, the results demonstrate that important clinical features can be extracted automatically with an acceptable error. Furthermore, the contraction classifier is able to predict the strength rating within a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>1</mn></mrow></semantics></math></inline-formula> scale point with 97% accuracy. These results demonstrate the system’s potential to enhance PFM training and rehabilitation by enabling women to monitor and improve their PFM contractions autonomously.https://www.mdpi.com/1424-8220/24/21/6937urinary incontinencerehabilitationpelvic floor muscle contractionvaginal dynamometerartificial intelligence |
| spellingShingle | Batoul El-Sayegh Chantale Dumoulin François Leduc-Primeau Mohamad Sawan Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction Sensors urinary incontinence rehabilitation pelvic floor muscle contraction vaginal dynamometer artificial intelligence |
| title | Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction |
| title_full | Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction |
| title_fullStr | Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction |
| title_full_unstemmed | Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction |
| title_short | Improving Pelvic Floor Muscle Training with AI: A Novel Quality Assessment System for Pelvic Floor Dysfunction |
| title_sort | improving pelvic floor muscle training with ai a novel quality assessment system for pelvic floor dysfunction |
| topic | urinary incontinence rehabilitation pelvic floor muscle contraction vaginal dynamometer artificial intelligence |
| url | https://www.mdpi.com/1424-8220/24/21/6937 |
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